Network node and method in a wireless communications network

ABSTRACT

A method performed by a method performed by a network node for performing admission control in a wireless communications network is provided. The network node serves a first and one or more second UEs. The network node receives (201) from the first UE, an access request for a radio resource for communication between the first UE and the network node. The network node further estimates (203) a first prediction of a requirement of the radio resource related to the access request, based on a measured initial data traffic between the network node and the first UE. The network node determines (205) a first threshold related to the first prediction, as a function of a measured data traffic load between the network node and the one or more second UEs. The network node then performs (206) admission control by deciding whether or not to admit the radio resource to the first UE, based on whether or not the first prediction exceeds the first threshold.

TECHNICAL FIELD

Embodiments herein relate to a network node and methods therein. Inparticular, they relate to admission control in a wirelesscommunications network.

BACKGROUND

In a typical wireless communication network, wireless devices, alsoknown as wireless communication devices, mobile stations, stations (STA)and/or User Equipments (UE), communicate via a Local Area Network suchas a WiFi network or a Radio Access Network (RAN) to one or more corenetworks (CN). The RAN covers a geographical area which is divided intoservice areas or cell areas, which may also be referred to as a beam ora beam group, with each service area or cell area being served by aradio network node such as a radio access node e.g., a W-Fi access pointor a radio base station (RBS), which in some networks may also bedenoted, for example, a NodeB, eNodeB (eNB), or gNB as denoted in 5G. Aservice area or cell area is a geographical area where radio coverage isprovided by the radio network node. The radio network node communicatesover an air interface operating on radio frequencies with the wirelessdevice within range of the radio network node.

Specifications for the Evolved Packet System (EPS), also called a FourthGeneration (4G) network, have been completed within the 3rd GenerationPartnership Project (3GPP) and this work continues in the coming 3GPPreleases, for example to specify a Fifth Generation (5G) network alsoreferred to as 5G New Radio (NR). The EPS comprises the EvolvedUniversal Terrestrial Radio Access Network (E-UTRAN), also known as theLong Term Evolution (LTE) radio access network, and the Evolved PacketCore (EPC), also known as System Architecture Evolution (SAE) corenetwork. E-UTRAN/LTE is a variant of a 3GPP radio access network whereinthe radio network nodes are directly connected to the EPC core networkrather than to RNCs used in 3G networks. In general, in E-UTRAN/LTE thefunctions of a 3G RNC are distributed between the radio network nodes,e.g. eNodeBs in LTE, and the core network. As such, the RAN of an EPShas an essentially “flat” architecture comprising radio network nodesconnected directly to one or more core networks, i.e. they are notconnected to RNCs. To compensate for that, the E-UTRAN specificationdefines a direct interface between the radio network nodes, thisinterface being denoted the X2 interface.

Multi-antenna techniques can significantly increase the data rates andreliability of a wireless communication system. The performance is inparticular improved if both the transmitter and the receiver areequipped with multiple antennas, which results in a Multiple-InputMultiple-Output (MIMO) communication channel. Such systems and/orrelated techniques are commonly referred to as MIMO.

In addition to faster peak Internet connection speeds, 5G planning aimsat higher capacity than current 4G, allowing higher number of mobilebroadband users per area unit, and allowing consumption of higher orunlimited data quantities in gigabyte per month and user. This wouldmake it feasible for a large portion of the population to streamhigh-definition media many hours per day with their mobile devices, whenout of reach of Wi-Fi hotspots. 5G research and development also aims atimproved support of machine to machine communication, also known as theInternet of things, aiming at lower cost, lower battery consumption andlower latency than 4G equipment.

Machine Learning (ML) is a field in computer science where a computerthrough algorithms and methods can be trained to learn certain patternsand their representation, so when confronted with similar data patternsthe computer can take decisions related to the data, e g predictions,classifications, and actions.

Admission Control (AC) is a control function in a closed system such ase.g. used in a wireless communications system, where access or resourcesare made available to requesting candidates such as UEs, which may bestraight forward if resources are abundant, and more selective whenresources get scarce.

Congestion Control (CC) is a control function in a closed system such ase.g. used in a wireless communications system, to avoid overloading byreducing the allocated resources. CC may be quite tolerant if resourcesare abundant, and more aggressive when resources get scarce.

How the admission and congestion of a system or resources are controlledis a major factor for determining the efficiency of the resourceutilization, and hence a large part of the performance of the system.

Traditional methods to handle out radio resources are at best usinghysteresis to even out variations in regulated stimuli levels. All usersare treated equally in terms of usage, and additional resource will inmany cases be handed out to wrong UEs, such as e.g. to UEs with lowcapacity demand resulting in that they never utilize the full capacityof the additional resource. In a loaded scenario users that requiresmore resources will instead be terminated or moved to less powerfulresource levels. In these cases the resources are not utilizedefficiently and the total system performance will be suffering.

SUMMARY

An object of embodiments herein is to improve the performance of awireless communications network.

According to a first aspect of embodiments herein, the object isachieved by a method performed by a network node for performingadmission control in a wireless communications network. The network nodeserves a first and one or more second UEs. The network node receivesfrom the first UE, an access request for a radio resource forcommunication between the first UE and the network node. The networknode further estimates a first prediction of a requirement of the radioresource related to the access request, based on a measured initial datatraffic between the network node and the first UE. The network nodedetermines a first threshold related to the first prediction, as afunction of a measured data traffic load between the network node andthe one or more second UEs. The network node then performs admissioncontrol by deciding whether or not to admit the radio resource to thefirst UE, based on whether or not the first prediction exceeds the firstthreshold.

According to a second aspect of embodiments herein, the object isachieved by a network node for performing admission control in awireless communications network. The network node is configured to servea first UE and one or more second UEs. The network node is furtherconfigured to:

-   -   Receive from the first UE, an access request for a radio        resource for communication between the first UE and the network        node,    -   estimate a first prediction of a requirement of the radio        resource related to the access request, based on a measured        initial data traffic between the network node and the first UE,    -   determine a first threshold related to the first prediction, as        a function of a measured data traffic load between the network        node and the one or more second UEs, and    -   perform admission control by deciding whether or not to admit        the radio resource to the first UE, based on whether or not the        first prediction exceeds the first threshold.

Since the network node estimates the first prediction based on initialdata traffic between the network node and the first UE, and determinesthe first threshold based on the data traffic load at the network nodeand the one or more second UEs, and then performs admission controlbased on whether or not the first prediction exceeds the firstthreshold, the admission control is performed dynamically based on acurrent condition instead of being static. This will in turn improve theperformance of a wireless communications network.

An advantage with embodiments herein is that they improve theutilization rate of the resources, leading to better system performance.Less resources will be wasted by more demanding users will beprioritized to the resources.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of embodiments herein are described in more detail withreference to attached drawings in which:

FIG. 1 is a schematic block diagram illustrating embodiments of awireless communications network.

FIGS. 2a and b are a flowchart depicting embodiments of a method in anetwork node.

FIG. 3 is a schematic block diagram illustrating embodiments implementedin a network node.

FIG. 4 is a schematic block diagram illustrating embodiments implementedin a network node.

FIG. 5 is a schematic block diagram illustrating embodiments implementedin a network node.

FIG. 6 is a schematic block diagram illustrating embodiments implementedin a network node.

FIG. 7 is a signaling diagram illustrating embodiments herein.

FIG. 8 is a diagram illustrating embodiments herein.

FIG. 9 is a schematic block diagram illustrating embodiments implementedin a network node.

FIG. 10 is a schematic block diagram illustrating embodimentsimplemented in a network node.

FIG. 11 is a schematic block diagram illustrating embodiments of anetwork node.

FIG. 12 schematically illustrates a telecommunication network connectedvia an intermediate network to a host computer.

FIG. 13 is a generalized block diagram of a host computer communicatingvia a base station with a user equipment over a partially wirelessconnection.

FIGS. 14 to 17 are flowcharts illustrating methods implemented in acommunication system including a host computer, a base station and auser equipment.

DETAILED DESCRIPTION

As a part of developing embodiments herein a problem will first beidentified and discussed.

As mentioned above, UEs are treated equally in terms of usage and noadaptation to e. g. different traffic patterns is included. This meansthat a UE is not granted resources based on the predicted requirement,so additional resource will in many cases be handed out to the wrongusers. This results in that the resources are not utilized efficientlyand the total system performance will be suffering. When using ML forcontrolling admission to a system or resources, the accuracy of thedecisions is a highly vital characteristic, and traditional ML models donot include methods for improving accuracy during fluctuatingconditions. In a dynamic environment, this is not sufficient, and moresituation adapted schemes are needed to utilize the full potential ofadmission control. Further, when used for over time in a dynamicenvironment more situation adapted schemes are needed to utilize thefull potential of the method.

Embodiments herein provide inherent characteristics of individual UEpredictions are utilized to improve performance for a measure e.g. overtime with the highest momentary importance, and thereby attain a dynamicand in some embodiments timely behavior automatically adapting tovarious conditions of the execution environment. This is to avoidarbitrariness in how the resources are distributed a mechanism ofprediction of resource usage needs per individual UE or connected UE.This mechanism may both be supporting the AC by predicting users withhigh demands, and the CC by predicting users with low demands for theremainder of the connection.

Example embodiments herein provide a method such as e.g. an ML modelbased AC to adapt to changing conditions and optimize its performance ina dynamic way, e.g. over time. The adaptation handles varying conditionsin an efficient way without any tuning or complex remodeling schemesinvolved. The solution is applicable for different models, varioustargets, and condition alterations of any kind.

In some embodiments of the method e.g. by means of an ML model, AC isperformed combined with CC that will improve the utilization rate of theresources, leading to better system performance. Less resources will bewasted—in the AC part by more demanding users are prioritized to theresources, and in the CC part by removing resources from users with lowdemands during the remainder of the connection.

Another important advantage is the reduction of configuration requiredfor the AC and the CC parts of the method. The resources areautomatically distributed to the users that will use them mostefficiently.

Embodiments herein relate to wireless communication networks in general.FIG. 1 is a schematic overview depicting a wireless communicationsnetwork 100. The wireless communications network 100 comprises one ormore RANs and one or more CNs. The wireless communications network 100may use NR but may further use a number of other different technologies,such as, 5G, NB-IoT, CAT-M, Wi-Fi, eMTC, Long Term Evolution (LTE),LTE-Advanced Wideband Code Division Multiple Access (WCDMA), GlobalSystem for Mobile communications/enhanced Data rate for GSM Evolution(GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), orUltra Mobile Broadband (UMB), just to mention a few possibleimplementations.

Network nodes operate in the wireless communications network 100, suchas a network node 110, providing radio coverage over a geographicalarea, a cell 11. The cell 11 may also be referred to as a service area,beam or a group of beams.

The network node 110 may be a transmission and reception point e.g. aradio access network node such as a base station, e.g. a radio basestation such as a NodeB, an evolved Node B (eNB, eNode B), an NR Node B(gNB), a base transceiver station, a radio remote unit, an Access PointBase Station, a base station router, a transmission arrangement of aradio base station, a stand-alone access point, a Wireless Local AreaNetwork (WLAN) access point or an Access Point Station (AP STA), anaccess controller, or any other network unit capable of communicatingwith a UE within the cell 11 served by the network node 110 dependinge.g. on the radio access technology and terminology used. The networknode 110 may be referred to as a serving radio network node andcommunicates with a UE 121, 122 with Downlink (DL) transmissions to theUE 121, 122 and Uplink (UL) transmissions from the UE 121, 122.

UEs such as e.g. a first UE 121 and one or more second UEs 122 operatein the wireless communications network 100. The UEs 121, 122 may e.g. bea mobile station, a wireless terminal, an NB-IoT device, an eMTC device,a CAT-M device, a WiFi device, an LTE device and an a non-access point(non-AP) STA, a STA, that communicates via a base station such as e.g.the network node 110, one or more Access Networks (AN), e.g. RAN, to oneor more core networks (CN). It should be understood by the skilled inthe art that “UE” is a non-limiting term which means any terminal,wireless communication terminal, user equipment, Device to Device (D2D)terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay,mobile tablets or even a small base station communicating within a cell.

Further network nodes operate in the wireless communications network100, such as a network node 130. The network node 130 may be an MMEwhich is a control node for an LTE access network and an NR network, anServing Gateway (SGW), and a Packet Data Network Gateway (PGW).

Methods according to embodiments herein may be performed by the networknode 110. As an alternative, a Distributed Node DN and functionality,e.g. comprised in a cloud 140 as shown in FIG. 4 may be used forperforming or partly performing the methods.

Example embodiments of a method performed by a network node 110 forperforming admission control in a wireless communications network 100,will now be described with reference to a flowchart depicted in FIGS. 2aand b . Whereof Actions 201-208 are shown in page 2 a, and Actions209-211 are shown in FIG. 2b . The network node 110 serves a first UE121 and one or more second UEs 122. The network node may comprise an MLmodel e.g. an ML model module.

The method comprises the following actions, which actions may be takenin any suitable order. Actions that are optional are presented in dashedboxes in FIG. 2.

Action 201

In an example scenario the first UE 121 has data to send or receive andtherefore needs radio resources from the network node 110 to access thewireless communications network 100. Therefore in some embodiments, thefirst UE 121 sends an access request to the network node 110. Thus, thenetwork node 110 receives an access request from the first UE 121. Theaccess request is for a radio resource for communication between thefirst UE 121 and the network node 110.

Action 202

In order to handle the admission control efficiently according toembodiments herein, the network node 110 needs to form an opinion aboutthe requested radio resources of first UE 121. This will be used lateron to predict the requirement of the radio resource that is requested bythe first UE 121. The network node 110 e.g. measures the initial datatraffic between the network node 110 and the and the first UE 121. As arespond to the access request, a default radio resource may be set up,sufficient to meet the requirements for UEs with low demands but may notinclude full user satisfaction for UEs with higher requirements. Inorder to measure the initial data traffic a first e.g. normal,connection must be set up, then over that first connection the initialdata traffic is measured and resource requirements are assessed.

This may not be performed, be performed or partly be performed by the MLmodel, depending on how the ML model is defined.

The measure may e.g. comprise initial service layer signalling, but alsodata traffic for the connected service. The initial data traffic may bemeasured by collecting characteristics of the transmitted data traffic,e.g. directions, volumes, timing information and statistics for thetransmitted data entities.

Action 203

The network node 110 then predicts the requirement of the radio resourcethat is requested by the first UE 121. The network node 110 estimates afirst prediction of a requirement of the radio resource related to theaccess request, based on a measured initial data traffic between thenetwork node 110 and the first UE 121. This will be used later on as aninput when deciding whether or not to admit an additional radio resourceto the first UE 121. In this way the network node 110 may e.g. predictsuch as e.g. learn whether it is probable that UE 121 is demanding ahigh Band Width (BW) and therefore e.g. demands additional resourcese.g. in the form of high BW for the communication. A high BW demandingUE when used herein means a UE that e.g. uses a service that requiresfast data communication for large data volumes in order to provide agood user experience. A result of this estimated first prediction may bea value e.g. the probability value that the first UE 121 demands high BWis 90%, or 50% or 30%, or as an alternative the probability that thefirst UE 121 does not require high BW is 90%, or 50% or 30%. A furtheralternative of a result of this estimated first prediction may be avalue e.g. of total data volume in either direction, the total servicetime for the connection, or the time to the next data burst, either as adirect value or the probability that the level will be over or under acertain limit. This action may be performed by the ML model e.g. in themodule residing in the network node 110.

Action 204

In order to handle the admission control even more efficiently accordingto embodiments herein, the network node 110 needs to form an opinionabout the available resources provided by the network node 110. It maybe assumed that the network node 110 provides a limited radio resourcepool. A large number of second UEs 122 communicating with the networknode 110 means a high data traffic load demanding a lot of the radioresources provided by the network node 110 and only a few, if any, radioresources available for additional UEs requesting access to the wirelesscommunications network 100. Vice versa, a small number of second UEs 122communicating with the network node 110 means a low data traffic loaddemanding only a little of the radio resources provided by the networknode 110 and a lot of radio resources are available for additional UEsrequesting access to the wireless communications network 100.

The network node 110 may therefore measure the data traffic load betweenthe network node 110 and the one or more second UEs 122. This may not beperformed, be performed or partly be performed by the ML model,depending on how the ML model is defined.

The wording data traffic load when used herein may e.g. comprise:Available radio resources in the network node 110 i.e. radio resourcesnot used at the moment, quantity of used radio resources in the networknode 110 i.e. radio resources that are used at the moment, data trafficpattern of the one or more second UEs 122, the number of connected UEs,the mobility activity i.e. the intensity of handovers, and the totaldata traffic transmitted.

The data traffic load may be measured by keeping track of the connecteddevices such as one or more second UEs 122, and their activities,continuously collecting characteristics of the individually andaggregated transmitted traffic, e.g. directions, pattern, volumes,timing information and statistics for the transmitted data entities.

Action 205

In order to handle the admission control dynamically according toembodiments herein, the network node 110 decides a first threshold, e.g.a limit, based on the current data traffic load which is to be used forthe admission control of the first UE 121. Thus the network node 110determines a first threshold related to the first prediction, as afunction of a measured data traffic load between the network node 110and the one or more second UEs 122. This action may be performed by theML model e.g. in the module residing in the network node 110.

In some embodiments, the first threshold comprises a probability, e.g. aprobability value, that the prediction of the requirement of theresource is above a first limit. In some alternative embodiments, thefirst threshold comprises any other statistical measure e.g. a standarddeviation, a statistical distribution, median value, etc.

According to embodiments herein, a first threshold may be determined foreach new UE requesting resources from the radio network node 110 andeach time a UE that was not admitted resources again requests resourcesfrom the radio network node 110. This means that the first threshold isdynamically determined and therefore may be changed dynamically. E.g. ata certain data traffic load the first threshold such as the probabilitylimit, for predicting high BW demand may be determined to 60%. Further,in a really demanding data traffic load situation the first thresholdfor predicting high BW demand may be determined to be 90%. According toembodiments herein the dynamically determined first threshold allows fora much better accuracy when performing admission control leading to thatalmost all scarce radio resources are assigned to the UEs that reallyneed them, i.e. the UEs with the highest BW demands. In an examplescenario the UEs with the highest BW demands are prioritized since thiswill yield the highest positive impact on user experience.

This action of determining the first threshold related to the firstprediction, as a function of a measured data traffic load between thenetwork node 110 and the one or more second UEs 122 may comprise todetermining the first threshold related to the first prediction as afunction of the measured data traffic load between the network node 110and the one or more second UEs 122. This e.g. means that when thetraffic load increases, also the first threshold increases.

Action 206

According to embodiments herein, the network node 110 now uses theestimated first prediction, and the determined first threshold as inputto perform the admission control. Thus the network node 110 performsadmission control by deciding whether or not to admit the radio resourceto the first UE 121, based on whether or not the first predictionexceeds the first threshold. This may not be performed, be performed orpartly be performed by the ML model, depending on how the ML model isdefined.

According to an example scenario the first threshold is decided to be70%. If the result of the estimated first prediction is a probabilityvalue that the first UE 121 demands high BW is 90%, this means that thefirst prediction of 90% exceeds the first threshold that is 70%. Thefirst UE 121 is therefore decided to be admitted access to the radioresources such as the additional resources. If the result of theestimated first prediction results in that the probability value thatthe first UE 121 demands high BW is 50%, this means that the firstprediction of 50% do not exceed the first threshold that is 70%. Thefirst UE 121 is therefore decided to not be admitted access to the radioresources such as the additional resources. Further, if the result ofthe estimated first prediction results in that the probability valuethat the first UE 121 demands high BW is 30%, this means that the firstprediction of 30% do not exceed the first threshold that is 70% either.The first UE 121 is therefore decided to not be admitted. According toan example scenario the first threshold is decided to be e.g. estimatedtotal connection data volume. If a larger volume is expected than thethreshold volume, decide to admit radio resources such as additionalradio resources, and vice versa.

Action 207

Since the data traffic load changes from time to time, the admissioncontrol procedure which according to embodiments herein is dynamic, maybe updated from time to time. This may be performed in fixedintermediate time intervals or in intermediate time intervalsdynamically determined based on a current the data traffic load.

Therefore, in some embodiments, one or more updates are performed withan intermediate time interval. In these embodiments, the network node110, may determine the intermediate time interval for performing therespective updates dynamically as a function of the measured datatraffic load between the network node 110 and the one or more second UEs122.

Action 208

When network node 110 has decided to not admit the radio resource to thefirst UE 121, the first UE 121 may send a new access request at a laterpoint in time. The network node 110 may accordingly perform one or moreupdates by repeating the following actions respective one or more times:

receiving 201 from the first UE 121, an access request for a radioresource for communication between the first UE 121 and the network node110,

estimating 203 a first prediction of a requirement of the radio resourcerelated to the access request, based on a measured initial data trafficbetween the network node 110 and the first UE 121,

determining 205 a first threshold related to the first prediction, as afunction of a measured data traffic load between the network node 110and the one or more second UEs 122, and

performing 206 admission control by deciding whether or not to admit theradio resource to the first UE 121, based on whether or not the firstprediction exceeds the first threshold.

When network node 110 has decided to admit the radio resource to thefirst UE 121, it may perform congestion control according to Actions209-211 below and shown in FIG. 2b , to further improve the usage of theradio resources.

Action 209

The network node 110 may estimate a second prediction of a forthcomingusage of the admitted radio resource, as a function of a measured datatraffic load between the network node 110 and the first UE 121.

Action 210

The network node 110 may further determine a second threshold related tothe second prediction, as a function of a measured data traffic loadbetween the network node 110 and the one or more second UEs 122.

Action 211

The network node 110 may then perform congestion control by decidingwhether or not to initiate a removal of the radio resource admitted tothe first UE 121, based on whether or not the second prediction is belowthe second threshold. This may not be performed, be performed or partlybe performed by the ML model, depending on how the ML model is defined.

Embodiments herein such as mentioned above will now be further describedand exemplified. The text below is applicable to and may be combinedwith any suitable embodiment described above.

As an example of how the solution may be used, a use case is consideredwith the network node 110 which e.g. is a Radio Base Station (RBS),mobile end users such as the first UE 121 and the second UEs 122, and alimited resource pool of additional radio resources in the RBS. Thepooled resources may be utilized by a limited number of UEs to enhancetheir individual performance, e g the bandwidth (BW), but each resourcemay only be utilized by a single UE such as e.g. the first UE 121. As avarying execution environment condition, the data traffic load in thenetwork node 110 is used. To determine how the resources are bestdistributed, i e how to find the UEs with for example, but not limitedto, the highest BW requirements, also referred to as the UE with thehighest BW demands, two ML models executing in the network node 110 maybe implemented to handle the Admission Control (AC) and in someembodiments the Congestion Control (CC), respectively. It is assumedthat the individual cost for the first UE 121 to utilize the resource islow, i e no tangible cost on battery or any other UE associatedresource. In the example the non-granted users may still have access butat a lower capability level.

The ML model in the AC part of the system may predict which UEs thatwill generate the highest demands on the resources, for example thehighest BW. E.g. whether or not the estimated first prediction of therequirement of the radio resource related to the access request of thefirst UE 121 exceeds the first threshold This means that the networknode 110 performs admission control by deciding to admit the radioresource to the first UE 121, when the first prediction exceeds thefirst threshold. E.g. other words, if the predicted BW for a UE is abovethe admission limit, the UE is granted, also referred to as admitted,additional capacity from the limited resource pool, as long as there areany available resources left. This means that the pool of radioresources may not always be fully utilized, but that the radio resourceutilization from the granted UEs will be high.

In some embodiments CC is performed after the AC. The ML model in the CCpart may supervise the utilization of the granted radio resources, ande.g. continuously monitor and predict near time utilization rate per UEsuch as for the first UE 121. This means that the network node 110performs congestion control by deciding whether or not to initiate aremoval of the radio resource admitted to the first UE 121, based onwhether or not the second prediction is below the second threshold. Sothe network node 110 decides to initiate a removal of the radio resourceadmitted to the first UE 121 when the second prediction is below thesecond threshold. Further, the network node 110 decides to not initiatea removal of the radio resource admitted to the first UE 121 when thesecond prediction is above or equal the second threshold. E.g. in otherwords, if any UE is predicted to have BW, for example, below the CCgrant limit, the resource is seen as forfeited and is no longer grantedto the UE. This mechanism also can lead to the resource pool not beingfully utilized at all times, but also that the individual resourceutilization is high.

The AC grant limit and the CC forfeit limit may be of the same ordifferent levels, depending on a desired hysteresis scheme.

The mechanism of give and remove grants to a user depending on thepredicted forthcoming resource utilization allows for a flexiblegranting scheme that follows a UE's varying traffic pattern, for examplethroughput burstiness.

Some first embodiments relate to the admission control based on whetheror not the first prediction exceeds the first threshold. Some secondembodiments relate to one or more updates that are performed e.g. withan intermediate time interval. Some third embodiments herein relate toan added congestion control procedure.

In a situation with low traffic load in the network node 110, a possiblestrategy for allocation of the resources in the resource pool may bethat each new UE such as the first UE 121, gets a resource to achievehigh bandwidth. The requirements on any prediction of the demanded BWper user are low, since the cost for a false prediction is low.

As the load in the network node 110 increases, the pooled resources inthe network node 110 start to get scarce, and thereby the importance ofa correct BW prediction by the AC increases. In embodiments herein thefirst threshold, which is referred to as the limit in the example, inthe ML model to predict high or low BW for a UE is dynamic. As mentionedabove an example of a the first threshold is the probability of theprediction. In one prior art example, if the probability is higher thane g 50%, high BW is always predicted since the limit is fixed. However,according to embodiments herein, these limits are determineddynamically, and may therefore be dynamically changed, e g at a certaindata traffic load at network node 110, the first threshold, e.g. theprobability limit, for predicting high BW may be decided to and/or bechanged to e.g. 60%. And in a really demanding load situation theprobability limit for high BW may be increased to 90%, allowing for amuch better accuracy of the prediction leading to almost all scarceresources assigned to the UEs that really need them.

As the load decreases, the limits such as the first threshold, may berelaxed, and these changes to the limits may be gradually following thesteering characteristic without any hard steps, allowing for optimalperformance in any data traffic load situation. In some embodiments thenetwork node 110 determines the first threshold related to the firstprediction, as a function of the measured data traffic load between thenetwork node 110 and the one or more second UEs 122. This may beperformed by aggregating or collecting characteristics of thetransmitted data traffic.

It is noteworthy that the environmental condition that steers the ACsuch as the RBS load in the example, the ML model indicator such as theprediction probability in the example, the resulting ML target such asthe prediction accuracy in the example, and the controlled artifact suchas the assigning of pooled BW resources in the example, are all examplesand not limited to these given herein. The embodiments herein includesusing one or more external characteristic measures to alter the ML modelresult used by AC, in order to achieve improved performance in a dynamicenvironment.

According to some embodiments herein, the AC algorithm e.g. comprisingthe actions 201-206 as described above, may be repeated per UE such asthe first UE 121. The AC algorithm may in some of these embodiments berepeated with statically intermediate time intervals, i.e. the samefixed intermediate time interval between the AC algorithms all the timewithout consider the load situation in the network node 110. As analternative, the AC algorithm may as an advantageous alternative berepeated with dynamically determined intermediate time intervals, i.e.deciding the intermediate time intervals between the AC algorithms fromtime to time and for each time of deciding taking the load situation inthe network node 110 into consideration. In this way, the AC algorithmmay be repeated with intermediate time intervals which are dynamicallychanged. In a really demanding traffic load situation at the networknode 110, intermediate time intervals between the AC algorithms may beshortened allowing for a much more frequent AC process and leading toalmost all scarce resources allocated to the UEs that really need them.The UEs that are really need the resources may e.g. be UEs that uses aservice that requires fast data communication for large data volumes inorder to provide a good user experience.

As the load decreases the intermediate time intervals between ACalgorithms may be extended, and these changes to the intermediate timeintervals may be gradually following the steering characteristic withoutany hard steps, allowing for optimal performance in any situation. Thenetwork node 110 may e.g. determine an intermediate time interval forperforming the respective updates dynamically as a function of themeasured data flow between the network node 110 and the one or moresecond UEs 122.

FIG. 3 is a block diagram that in a schematic way shows how the AC worksin a system such as the wireless communications network 100. UEs,referred to as Users in FIG. 3, such as the first UE 121, send requestsrequesting access to radio resources referred to as resources in FIG. 3.The network node 110 performs AC and either grants or denies therequests. I.e. the network node 110 performs admission control bydeciding whether or not to admit the radio resource to the first UE 120,based on whether or not the first prediction exceeds the firstthreshold. The admitted UEs 121 which is referred to as granted users inFIG. 3 are included in a pool of resource usage. Denied users may send arequest again, wait or give up.

According to some embodiments mentioned above, the dynamic intermediateinterval timing may be provided for the renewed requests depending onthe environmental situation such as the data traffic load, as well asdynamic levels for granting the access such the second thresholds, alsobased on the environmental situation such as the data traffic load.

FIG. 4 is a block diagram that in a schematic way shows how one or moreEnvironmental Conditions such as the data traffic load is used as inputto a ML Model and thereby impacts an output to the AC in the networknode 110. As an example the AC performed by the network node 110, maycontrol the usage of a limited resource pool in the network node 110,and the network node 110 may by means of the ML model decide which UEsthat is to be given radio resources. The decision may be based on aninitial observation period of each user's behavior, referred to asmeasuring the initial data traffic between the network node 110 and theand the first UE 121 in Action 202 above.

FIG. 5 is a block diagram that in a schematic way shows in more detailhow Environmental Condition signals such as data traffic load signalsare used by a regulation addition to the ML Model, by producing a ModelIndicator that e.g. the core of the ML Model may use to alter itsdecision of admitting access to any type of resources in the networknode 110. The decision is then sent to the Admission Control to be usedwhen deciding whether or not resources should be granted. The ML Modelperformance such as accuracy or any other model performance statisticmay be constantly monitored.

The wording regulation addition when used herein means a complementaryfunction that uses input from the environment, traffic, network node orany other related artifact that can assist in providing additional inputor conditions to the regulation.

The wording Model Indicator when used herein means a complementarycondition or input parameter to the regulation.

The wording core of the ML Model when used herein means the actualprediction function that, by being pre-trained on similar input data,from a set of input data may give an accurate prediction of therequested resource quantity.

The wording ML Model performance when used herein means a validstatistic of how well the ML model performs, e.g. how accurate theprediction is or how fast the prediction is determined.

FIG. 6 is a block diagram that in a schematic way shows how embodimentsherein may be implemented and used, in the example scenario as describedabove, such that e.g. the radio resources are allocated to the UEs thatcan make best use of them. The initial data traffic load of the first UE121 is measured in an initial Session Measurement. The network node 110may then by means of the ML Model estimating the first prediction of therequirement of the radio resource related to the access request, basedon the measured initial data traffic between the network node 110 andthe first UE 121, as referred to in Action 203 above. This is the firstinput to the AC in FIG. 6. The regulation such as e.g. the firstthreshold is determined based on measured data traffic load of thesecond UEs 122, as referred to in Action 204 above. This is the secondinput to the AC in FIG. 6. The ML Model may thus predict whether thefirst UE 121 will benefit from additional resources, which then would beadmitted by and allocated in the network node 110.

The data traffic load between the network node 110 and the one or moresecond UEs 122 and the initial data traffic between the network node 110and the and the first UE 121 are measured again at intermediate timeintervals dependent on environmental conditions such as the data trafficload, and the ML Model may then predict again whether the first UE 121will benefit from additional resources and grants or rejects the accessto these radio resources. The decision is taking the environmentalconditions such as the data traffic load into account, thereby enablinga dynamic access scheme dependent on the current situation.

FIG. 7 is a sequence diagram that in a schematic way illustrates asignaling flow within the ML model module residing in the network node110 according to an example embodiment. The environmental condition ispresented to the Regulation part of the solution, this means that theregulation such as e.g. the first threshold is determined based onmeasured data traffic load of the second UEs 122, as referred to inAction 204. The regulation then sends an updated model indicator whichin this example is the determined first threshold, to the predictionpart of the ML Model for estimation of the first prediction of therequirement of the radio resource related to the access request, basedon a measured initial data traffic between the network node 110 and thefirst UE 121 as described in Action 203. This results in an AdmissionControl signal that defines the decision from the ML Model such as whenthe network node 110 performs admission control by deciding whether ornot to admit the radio resource to the first UE 121, based on whether ornot the first prediction exceeds the first threshold as described inAction 206.

This process is repeated with dynamic time intervals.

FIG. 8 illustrates an example how embodiments herein may working as adiagram of a Precision-at-Top distribution for the network node 110 suchas its ML model for prediction of the first and/or second threshold. Thediagram shows relationship between accuracy and probability. Theaccuracy axis of the diagram refers to the accuracy of the part of thepredictions above the breaking point in the graph, the breaking pointmeaning the point where the dashed lines meet. The probability of thediagram refers to the estimated first prediction which may be aprobability that the conditions for granting data resources such asadditional resources are fulfilled. The default value for prior artdecisions is the probability 0.5, this is shown in the diagram forcomparison with embodiments herein. The wording Precision-at-Top whenused herein means the resulting precision distribution when predictionoutcomes are sorted in falling probability and the resulting precisionfor the share of the samples from the top to any given point arecalculated. By using the Environmental Condition as a trigger, the firstand/or second threshold e.g. the probability limit, for theclassification may be different from 0.5 when the data traffic load istaken into consideration, such as e g 0.8 which would yield a muchhigher percentage of correctly predicted targets. The wording correctlypredicted targets when used herein means the share of predictions thatare shown to be correct, meaning that when the outcome of the predictedactivity or entity is revealed whether or not this outcome correspondswith the predicted value. Both the Probability and the Accuracy may bechanged to other characteristics for other applications of embodimentsherein. Further, both the Probability and the Accuracy may be changedover time to other characteristics for other applications of embodimentsherein.

Embodiments herein work well in a distributed computing environment, andany of the building blocks may be implemented on a separate Hard Ware(HW) unit or server, or as a separate thread or process in any operatingsystem environment, or as a virtual block executing in a cloudconfiguration. FIG. 9 shows an example scenario of how the includedparts may be configured to operate over separate HW/Soft Ware(SW)/Virtual units.

FIG. 10 is a block diagram that in a schematic way shows how the AC incombination with CC work in a system such as the wireless communicationsnetwork 100. UEs, referred to as Users in FIG. 11, such as the first UE121, send requests requesting access to radio resources referred to asresources in FIG. 11. The network node 110 performs AC and either grantsor denies the requests. I.e. the network node 110 performs admissioncontrol by deciding whether or not to admit the radio resource to thefirst UE 120, based on whether or not the first prediction exceeds thefirst threshold. The admitted UEs 121 which is referred to as grantedusers in FIG. 11 are included in a pool of resource usage. Denied usersmay send a request again, wait or give up.

According to some embodiments, after users are being admitted, alsoreferred to as granted, to resource usage, the UEs forthcoming resourceutilization are predicted. This means that the network node 110 mayestimate a second prediction of a forthcoming usage of the allocatedradio resource, based on a measured data traffic between the networknode 110 and the first UE 121 as described above in action 209, anddetermine a second threshold related to the first prediction, as afunction of a measured data traffic load as described above in action210.

The network node 110 then performs congestion control by decidingwhether or not to initiate a removal of the radio resource admitted tothe first UE 120, based on whether or not the second prediction is belowthe second threshold as described above in action 211.

This means that the grant may be removed if utilization is predicted tobe below a certain level.

In some embodiments, the traffic is continuously monitored by thenetwork node 110 and will predict whether the UE will benefit fromadditional resources or not e.g. by means of ML Models in the AC and CCparts. The AC part may request resources in the network node 110 to beallocated to a specific UE such as the first UE 121, and the CC part mayrequest resource removal for this UE.

In an example scenario, User Data such as a measure of an initial datatraffic, is presented in an AC part in the network node 110, which incase it predicts high resource utilization sends a Resource Grant to aResource Handler in the network node 110. The Resource Handler may thengive resource access to the specific user such as the first UE 121. Atthat point the CC part in the network node 110 starts predicting theforthcoming resource utilization, and in case a too low utilization ispredicted a Grant Removal indication is sent to the Resource Handler inthe network node 110, which then may remove the resource from the thisUE. The AC part may then start predicting resource demands again, and soon.

Embodiments herein provide a way of dealing with varying conditions e.g.by means of an ML model, to dynamically adjust its behavior depending onthe loaded cell conditions, in some embodiments also over time, andimprove the performance in the Admission Control when it matters themost. Modern traffic patterns are bursty and vary significantly overlong and short usage terms. According to embodiments herein, both anindividual UE's demands may be met, as well as the system's demand suchas wireless communications network's 100 demand for efficient usage ofthe resources. Embodiments herein provide UE differentiation.

An example use case is a situation when loaded cell conditions requirehigher precision when predicting UEs with high bandwidth demands.Another example use case is a situation when rerunning the AC with adynamic time interval depending on the loaded cell conditions. A furtherexample use case relating to some embodiments herein is a situationwhere cell resources may be taken away if the demand of the first UE 121is predicted to decrease.

However, embodiments herein may be applied in a much broader perspectiveand may include any AC applications for any different environmentcondition, various prediction targets, any model characteristics, andany model performance metrics. The dynamic connection between theenvironmental conditions, the ML model bi-products like probability, andthe ML model performance metrics is a fundamental part, and that thisconnection will produce better results when needed the most.

To perform the method actions for performing admission control in awireless communications network 100, the network node 110 may comprisethe arrangement depicted in FIG. 11. As mentioned above, the networknode 110 is configured to serve the first UE 121 and one or more secondUEs 122.

The network node 110 may comprise an input and output interface 1100configured to communicate e.g. with the network node 110. The input andoutput interface 1100 may comprise a wireless receiver (not shown) and awireless transmitter not (shown).

The network node 110 is further configured to, e.g. by means of areceiving unit 1110 comprised in the network node 110, receive from thefirst UE 121, an access request for a radio resource for communicationbetween the first UE 121 and the network node 110.

The network node 110 is further configured to, e.g. by means of anestimating unit 1120 comprised in the network node 110, estimate a firstprediction of a requirement of the radio resource related to the accessrequest, based on a measured initial data traffic between the networknode 110 and the first UE 121.

The network node 110 is further configured to, e.g. by means of adetermining unit 1130 comprised in the network node 110, determine afirst threshold related to the first prediction, as a function of ameasured data traffic load between the network node 110 and the one ormore second UEs 122. The first threshold may be adapted to comprise aprobability that the prediction of the requirement of the resource isabove a first limit.

The network node 110 may further be configured to, e.g. by means of thedetermining unit 1130 comprised in the network node 110, determine thefirst threshold related to the first prediction, as a function of ameasured data traffic load between the network node 110 and the one ormore second UEs 122 by determining the first threshold related to thefirst prediction, as a function of the measured data traffic loadbetween the network node 110 and the one or more second UEs 122.

The network node 110 is further configured to, e.g. by means of aperforming unit 1140 comprised in the network node 110, performadmission control by deciding whether or not to admit the radio resourceto the first UE 121, based on whether or not the first predictionexceeds the first threshold.

The network node 110 may further be configured to, e.g. by means of theperforming unit 1140 comprised in the network node 110, when decided tonot admit the radio resource to the first UE 121, perform one or moreupdates by repeating the following actions respective one or more times:Receive from the first UE 121, an access request for a radio resourcefor communication between the first UE 121 and the network node 110,estimate a first prediction of a requirement of the radio resourcerelated to the access request, based on a measured initial data trafficbetween the network node 110 and the first UE 121, determine a firstthreshold related to the first prediction, as a function of a measureddata traffic load between the network node 110 and the one or moresecond UEs 122, and perform admission control by deciding whether or notto admit the radio resource to the first UE 121, based on whether or notthe first prediction exceeds the first threshold.

In some embodiments, the respective updates are adapted to be performedwith an intermediate time interval. In these embodiments, the networknode 110 may further be configured to, e.g. by means of the determiningunit 1130 comprised in the network node 110, determine an intermediatetime interval for performing the respective updates dynamically as afunction of the measured data traffic load between the network node 110and the one or more second UEs 122.

The network node 110 may further be configured to, e.g. by means of ameasuring unit 1150 comprised in the network node 110, to any one ormore out of: Measure the initial data traffic between the network node110 and the and the first UE 121, and measure a data traffic loadbetween the network node 110 and the one or more second UEs 122.

In an example scenario, when it is decided to admit the radio resourceto the first UE 121, the network node 110 may further being configuredto the following:

E.g. by means of the estimating unit 1120 comprised in the network node110, estimate a second prediction of a forthcoming usage of the admittedradio resource, as a function of a measured data traffic load betweenthe network node 110 and the first UE 121,

e.g. by means of the determining unit 1130 comprised in the network node110, determine a second threshold related to the second prediction, as afunction of a measured data traffic load between the network node 110and the one or more second UEs 122, and

e.g. by means of the performing unit 1140 comprised in the network node110, perform congestion control by deciding whether or not to initiate aremoval of the radio resource admitted to the first UE 121, based onwhether or not the second prediction is below the second threshold.

The embodiments herein may be implemented through a respective processoror one or more processors, such as a processor 1160 of a processingcircuitry in the network node 110 depicted in FIG. 11, together withrespective computer program code for performing the functions andactions of the embodiments herein. The program code mentioned above mayalso be provided as a computer program product, for instance in the formof a data carrier carrying computer program code for performing theembodiments herein when being loaded into the network node 110. One suchcarrier may be in the form of a CD ROM disc. It is however feasible withother data carriers such as a memory stick. The computer program codemay furthermore be provided as pure program code on a server anddownloaded to the network node 110.

The network node 110 may further comprise a memory 1170 comprising oneor more memory units. The memory comprises instructions executable bythe processor in the network node 110. The memory 1170 is arranged to beused to store e.g. data, configurations, thresholds, predictions,determined intermediate time intervals, measurements, and applicationsto perform the methods herein when being executed in the network node110.

In some embodiments, a respective computer program 1180 comprisesinstructions, which when executed by the respective at least oneprocessor 1180, cause the at least one processor 1160 of the networknode 110 to perform the actions above.

In some embodiments, a respective carrier 1190 comprises the respectivecomputer program 1180, wherein the carrier is one of an electronicsignal, an optical signal, an electromagnetic signal, a magnetic signal,an electric signal, a radio signal, a microwave signal, or acomputer-readable storage medium.

Those skilled in the art will also appreciate that the units in thenetwork node 110 mentioned above may refer to a combination of analogand digital circuits, and/or one or more processor configured withsoftware and/or firmware, e.g. stored in the network node 110 that whenexecuted by the respective one or more processors such as the processorsdescribed above. One or more of these processors, as well as the otherdigital hardware, may be included in a single Application-SpecificIntegrated Circuitry (ASIC), or several processors and various digitalhardware may be distributed among several separate components, whetherindividually packaged or assembled into a system-on-a-chip (SoC).

Further Extensions and Variations

With reference to FIG. 12, in accordance with an embodiment, acommunication system includes a telecommunication network 3210 such asthe wireless communications network 100, e.g. a NR network, such as a3GPP-type cellular network, which comprises an access network 3211, suchas a radio access network, and a core network 3214. The access network3211 comprises a plurality of base stations 3212 a, 3212 b, 3212 c, suchas the network node 110, access nodes, AP STAs NBs, eNBs, gNBs or othertypes of wireless access points, each defining a corresponding coveragearea 3213 a, 3213 b, 3213 c. Each base station 3212 a, 3212 b, 3212 c isconnectable to the core network 3214 over a wired or wireless connection3215. A first user equipment (UE) e.g. the first UE 121 such as a Non-APSTA 3291 located in coverage area 3213 c is configured to wirelesslyconnect to, or be paged by, the corresponding base station 3212 c. Asecond UE 3292 e.g. the wireless device 122 such as a Non-AP STA incoverage area 3213 a is wirelessly connectable to the corresponding basestation 3212 a. While a plurality of UEs 3291, 3292 are illustrated inthis example, the disclosed embodiments are equally applicable to asituation where a sole UE is in the coverage area or where a sole UE isconnecting to the corresponding base station 3212.

The telecommunication network 3210 is itself connected to a hostcomputer 3230, which may be embodied in the hardware and/or software ofa standalone server, a cloud-implemented server, a distributed server oras processing resources in a server farm. The host computer 3230 may beunder the ownership or control of a service provider, or may be operatedby the service provider or on behalf of the service provider. Theconnections 3221, 3222 between the telecommunication network 3210 andthe host computer 3230 may extend directly from the core network 3214 tothe host computer 3230 or may go via an optional intermediate network3220. The intermediate network 3220 may be one of, or a combination ofmore than one of, a public, private or hosted network; the intermediatenetwork 3220, if any, may be a backbone network or the Internet; inparticular, the intermediate network 3220 may comprise two or moresub-networks (not shown).

The communication system of FIG. 12 as a whole enables connectivitybetween one of the connected UEs 3291, 3292 and the host computer 3230.The connectivity may be described as an over-the-top (OTT) connection3250. The host computer 3230 and the connected UEs 3291, 3292 areconfigured to communicate data and/or signaling via the OTT connection3250, using the access network 3211, the core network 3214, anyintermediate network 3220 and possible further infrastructure (notshown) as intermediaries. The OTT connection 3250 may be transparent inthe sense that the participating communication devices through which theOTT connection 3250 passes are unaware of routing of uplink and downlinkcommunications. For example, a base station 3212 may not or need not beinformed about the past routing of an incoming downlink communicationwith data originating from a host computer 3230 to be forwarded (e.g.,handed over) to a connected UE 3291. Similarly, the base station 3212need not be aware of the future routing of an outgoing uplinkcommunication originating from the UE 3291 towards the host computer3230.

Example implementations, in accordance with an embodiment, of the UE,base station and host computer discussed in the preceding paragraphswill now be described with reference to FIG. 13. In a communicationsystem 3300, a host computer 3310 comprises hardware 3315 including acommunication interface 3316 configured to set up and maintain a wiredor wireless connection with an interface of a different communicationdevice of the communication system 3300. The host computer 3310 furthercomprises processing circuitry 3318, which may have storage and/orprocessing capabilities. In particular, the processing circuitry 3318may comprise one or more programmable processors, application-specificintegrated circuits, field programmable gate arrays or combinations ofthese (not shown) adapted to execute instructions. The host computer3310 further comprises software 3311, which is stored in or accessibleby the host computer 3310 and executable by the processing circuitry3318. The software 3311 includes a host application 3312. The hostapplication 3312 may be operable to provide a service to a remote user,such as a UE 3330 connecting via an OTT connection 3350 terminating atthe UE 3330 and the host computer 3310. In providing the service to theremote user, the host application 3312 may provide user data which istransmitted using the OTT connection 3350.

The communication system 3300 further includes a base station 3320provided in a telecommunication system and comprising hardware 3325enabling it to communicate with the host computer 3310 and with the UE3330. The hardware 3325 may include a communication interface 3326 forsetting up and maintaining a wired or wireless connection with aninterface of a different communication device of the communicationsystem 3300, as well as a radio interface 3327 for setting up andmaintaining at least a wireless connection 3370 with a UE 3330 locatedin a coverage area (not shown in FIG. 13) served by the base station3320. The communication interface 3326 may be configured to facilitate aconnection 3360 to the host computer 3310. The connection 3360 may bedirect or it may pass through a core network (not shown in FIG. 13) ofthe telecommunication system and/or through one or more intermediatenetworks outside the telecommunication system. In the embodiment shown,the hardware 3325 of the base station 3320 further includes processingcircuitry 3328, which may comprise one or more programmable processors,application-specific integrated circuits, field programmable gate arraysor combinations of these (not shown) adapted to execute instructions.The base station 3320 further has software 3321 stored internally oraccessible via an external connection.

The communication system 3300 further includes the UE 3330 alreadyreferred to. Its hardware 3335 may include a radio interface 3337configured to set up and maintain a wireless connection 3370 with a basestation serving a coverage area in which the UE 3330 is currentlylocated. The hardware 3335 of the UE 3330 further includes processingcircuitry 3338, which may comprise one or more programmable processors,application-specific integrated circuits, field programmable gate arraysor combinations of these (not shown) adapted to execute instructions.The UE 3330 further comprises software 3331, which is stored in oraccessible by the UE 3330 and executable by the processing circuitry3338. The software 3331 includes a client application 3332. The clientapplication 3332 may be operable to provide a service to a human ornon-human user via the UE 3330, with the support of the host computer3310. In the host computer 3310, an executing host application 3312 maycommunicate with the executing client application 3332 via the OTTconnection 3350 terminating at the UE 3330 and the host computer 3310.In providing the service to the user, the client application 3332 mayreceive request data from the host application 3312 and provide userdata in response to the request data. The OTT connection 3350 maytransfer both the request data and the user data. The client application3332 may interact with the user to generate the user data that itprovides. It is noted that the host computer 3310, base station 3320 andUE 3330 illustrated in FIG. 13 may be identical to the host computer3230, one of the base stations 3212 a, 3212 b, 3212 c and one of the UEs3291, 3292 of FIG. 12, respectively. This is to say, the inner workingsof these entities may be as shown in FIG. 13 and independently, thesurrounding network topology may be that of FIG. 12.

In FIG. 13, the OTT connection 3350 has been drawn abstractly toillustrate the communication between the host computer 3310 and the useequipment 3330 via the base station 3320, without explicit reference toany intermediary devices and the precise routing of messages via thesedevices. Network infrastructure may determine the routing, which it maybe configured to hide from the UE 3330 or from the service provideroperating the host computer 3310, or both. While the OTT connection 3350is active, the network infrastructure may further take decisions bywhich it dynamically changes the routing (e.g., on the basis of loadbalancing consideration or reconfiguration of the network).

The wireless connection 3370 between the UE 3330 and the base station3320 is in accordance with the teachings of the embodiments describedthroughout this disclosure. One or more of the various embodimentsimprove the performance of OTT services provided to the UE 3330 usingthe OTT connection 3350, in which the wireless connection 3370 forms thelast segment. More precisely, the teachings of these embodiments mayimprove the data rate, latency, power consumption and thereby providebenefits such as user waiting time, relaxed restriction on file size,better responsiveness, extended battery lifetime.

A measurement procedure may be provided for the purpose of monitoringdata rate, latency and other factors on which the one or moreembodiments improve. There may further be an optional networkfunctionality for reconfiguring the OTT connection 3350 between the hostcomputer 3310 and UE 3330, in response to variations in the measurementresults. The measurement procedure and/or the network functionality forreconfiguring the OTT connection 3350 may be implemented in the software3311 of the host computer 3310 or in the software 3331 of the UE 3330,or both. In embodiments, sensors (not shown) may be deployed in or inassociation with communication devices through which the OTT connection3350 passes; the sensors may participate in the measurement procedure bysupplying values of the monitored quantities exemplified above, orsupplying values of other physical quantities from which software 3311,3331 may compute or estimate the monitored quantities. The reconfiguringof the OTT connection 3350 may include message format, retransmissionsettings, preferred routing etc.; the reconfiguring need not affect thebase station 3320, and it may be unknown or imperceptible to the basestation 3320. Such procedures and functionalities may be known andpracticed in the art. In certain embodiments, measurements may involveproprietary UE signaling facilitating the host computer's 3310measurements of throughput, propagation times, latency and the like. Themeasurements may be implemented in that the software 3311, 3331 causesmessages to be transmitted, in particular empty or ‘dummy’ messages,using the OTT connection 3350 while it monitors propagation times,errors etc.

FIG. 14 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station such as aAP STA, and a UE such as a Non-AP STA which may be those described withreference to FIGS. 32 and 33. For simplicity of the present disclosure,only drawing references to FIG. 14 will be included in this section. Ina first action 3410 of the method, the host computer provides user data.In an optional subaction 3411 of the first action 3410, the hostcomputer provides the user data by executing a host application. In asecond action 3420, the host computer initiates a transmission carryingthe user data to the UE. In an optional third action 3430, the basestation transmits to the UE the user data which was carried in thetransmission that the host computer initiated, in accordance with theteachings of the embodiments described throughout this disclosure. In anoptional fourth action 3440, the UE executes a client applicationassociated with the host application executed by the host computer.

FIG. 15 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station such as aAP STA, and a UE such as a Non-AP STA which may be those described withreference to FIGS. 32 and 33. For simplicity of the present disclosure,only drawing references to FIG. 15 will be included in this section. Ina first action 3510 of the method, the host computer provides user data.In an optional subaction (not shown) the host computer provides the userdata by executing a host application. In a second action 3520, the hostcomputer initiates a transmission carrying the user data to the UE. Thetransmission may pass via the base station, in accordance with theteachings of the embodiments described throughout this disclosure. In anoptional third action 3530, the UE receives the user data carried in thetransmission.

FIG. 16 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station such as aAP STA, and a UE such as a Non-AP STA which may be those described withreference to FIGS. 32 and 33. For simplicity of the present disclosure,only drawing references to FIG. 16 will be included in this section. Inan optional first action 3610 of the method, the UE receives input dataprovided by the host computer. Additionally or alternatively, in anoptional second action 3620, the UE provides user data. In an optionalsubaction 3621 of the second action 3620, the UE provides the user databy executing a client application. In a further optional subaction 3611of the first action 3610, the UE executes a client application whichprovides the user data in reaction to the received input data providedby the host computer. In providing the user data, the executed clientapplication may further consider user input received from the user.Regardless of the specific manner in which the user data was provided,the UE initiates, in an optional third subaction 3630, transmission ofthe user data to the host computer. In a fourth action 3640 of themethod, the host computer receives the user data transmitted from theUE, in accordance with the teachings of the embodiments describedthroughout this disclosure.

FIG. 17 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station such as aAP STA, and a UE such as a Non-AP STA which may be those described withreference to FIGS. 32 and 33. For simplicity of the present disclosure,only drawing references to FIG. 17 will be included in this section. Inan optional first action 3710 of the method, in accordance with theteachings of the embodiments described throughout this disclosure, thebase station receives user data from the UE. In an optional secondaction 3720, the base station initiates transmission of the receiveduser data to the host computer. In a third action 3730, the hostcomputer receives the user data carried in the transmission initiated bythe base station.

When using the word “comprise” or “comprising” it shall be interpretedas non-limiting, i.e. meaning “consist at least of”.

The embodiments herein are not limited to the above described preferredembodiments. Various alternatives, modifications and equivalents may beused.

1. A method performed by a network node for performing admission controlin a wireless communications network, which network node serves a firstuser equipment (UE) and one or more second UEs, the method comprising:after the network node receives from the first UE an access request fora radio resource for communication between the first UE and the networknode, the network node estimating a first prediction of a requirement ofthe radio resource related to the access request based on a measuredinitial data traffic between the network node and the first UE;determining a first threshold related to the first prediction as afunction of a measured data traffic load between the network node andthe one or more second UEs; and performing admission control by decidingwhether or not to admit the radio resource to the first UE based onwhether or not the first prediction exceeds the first threshold.
 2. Themethod of claim 1, wherein the first threshold comprises a probabilitythat the prediction of the requirement of the resource is above a firstlimit.
 3. The method of claim 1, further comprising at least one of:measuring the initial data traffic between the network node and thefirst UE, or measuring a data traffic load between the network node andthe one or more second UEs.
 4. The method of claim 1, when decided tonot admit the radio resource to the first UE: performing one or moreupdates by repeating the following actions one or more times: receivingfrom the first UE, an access request for a radio resource forcommunication between the first UE and the network node; estimating afirst prediction of a requirement of the radio resource related to theaccess request, based on a measured initial data traffic between thenetwork node and the first UE; determining a first threshold related tothe first prediction, as a function of a measured data traffic loadbetween the network node and the one or more second UEs; and performingadmission control by deciding whether or not to admit the radio resourceto the first UE, based on whether or not the first prediction exceedsthe first threshold.
 5. The method of claim 4, wherein the respectiveupdates are performed with an intermediate time interval, the methodfurther comprising: determining an intermediate time interval forperforming the respective updates dynamically as a function of themeasured data traffic load between the network node and the one or moresecond UEs.
 6. The method of claim 1, wherein determining the firstthreshold related to the first prediction as a function of a measureddata traffic load between the network node and the one or more secondUEs comprises: determining the first threshold related to the firstprediction as a function of the measured data traffic load between thenetwork node and the one or more second UEs.
 7. The method of claim 1,wherein it is decided to admit the radio resource to the first UE, themethod further comprising: estimating a second prediction of aforthcoming usage of the allocated radio resource, as a function of ameasured data traffic load between the network node and the first UE;determining a second threshold related to the second prediction, as afunction of a measured data traffic load between the network node andthe one or more second UEs; and performing congestion control bydeciding whether or not to initiate a removal of the radio resourceadmitted to the first UE, based on whether or not the second predictionis below the second threshold.
 8. A non-transitory computer readablemedium storing a computer program comprising instructions, which whenexecuted by a processor, causes the processor to perform the method ofclaim
 1. 9. (canceled)
 10. A network node for performing admissioncontrol in a wireless communications network, which network node isconfigured to serve a first user equipment (UE) and one or more secondUEs, the network node comprising: a receiver for receiving from thefirst UE an access request for a radio resource for communicationbetween the first UE and the network node; and processing circuitrycoupled to the receiver, the processing circuitry being configured to:estimate a first prediction of a requirement of the radio resourcerelated to the access request based on a measured initial data trafficbetween the network node and the first UE; determine a first thresholdrelated to the first prediction as a function of a measured data trafficload between the network node and the one or more second UEs; andperform admission control by deciding whether or not to admit the radioresource to the first UE based on whether or not the first predictionexceeds the first threshold.
 11. The network node of claim 10, whereinthe first threshold comprises a probability that the prediction of therequirement of the resource is above a first limit.
 12. The network nodeof claim 10, further being configured to: measure the initial datatraffic between the network node and the first UE, and/or measure a datatraffic load between the network node and the one or more second UEs.13. The network node of claim 10, further being configured to, whendecided to not admit the radio resource to the first UE: perform one ormore updates by repeating the following actions one or more times:receive from the first UE an access request for a radio resource forcommunication between the first UE and the network node; estimate afirst prediction of a requirement of the radio resource related to theaccess request based on a measured initial data traffic between thenetwork node and the first UE; determine a first threshold related tothe first prediction as a function of a measured data traffic loadbetween the network node and the one or more second UEs; and performadmission control by deciding whether or not to admit the radio resourceto the first UE, based on whether or not the first prediction exceedsthe first threshold.
 14. The network node of claim 13, wherein therespective updates are adapted to be performed with an intermediate timeinterval, the network node further being configured to: determine anintermediate time interval for performing the respective updatesdynamically as a function of the measured data traffic load between thenetwork node and the one or more second UEs.
 15. The network node ofclaim 10, wherein the network node further being configured to determinethe first threshold related to the first prediction as a function of ameasured data traffic load between the network node and the one or moresecond UEs by: determining the first threshold related to the firstprediction as a function of the measured data traffic load between thenetwork node and the one or more second UEs.
 16. The network node ofclaim 10, further being configured to, when it is decided to admit theradio resource to the first UE: estimate a second prediction of aforthcoming usage of the admitted radio resource, as a function of ameasured data traffic load between the network node and the first UE;determine a second threshold related to the second prediction, as afunction of a measured data traffic load between the network node andthe number of UEs; and perform congestion control by deciding whether ornot to initiate a removal of the radio resource admitted to the firstUE, based on whether or not the second prediction is below the secondthreshold.